Overview

Dataset statistics

Number of variables17
Number of observations17544
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory168.0 B

Variable types

NUM11
BOOL3
CAT2
DATE1

Warnings

dteday has a high cardinality: 731 distinct values High cardinality
atemp is highly correlated with tempHigh correlation
temp is highly correlated with atempHigh correlation
cnt is highly correlated with registeredHigh correlation
registered is highly correlated with cntHigh correlation
dteday is uniformly distributed Uniform
datetime has unique values Unique
hr has 731 (4.2%) zeros Zeros
weekday has 2520 (14.4%) zeros Zeros
windspeed has 2183 (12.4%) zeros Zeros
casual has 1623 (9.3%) zeros Zeros

Reproduction

Analysis started2020-10-30 10:02:44.180366
Analysis finished2020-10-30 10:03:02.599633
Duration18.42 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

datetime
Date

UNIQUE

Distinct17544
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size137.2 KiB
Minimum2011-01-01 00:00:00
Maximum2012-12-31 23:00:00
2020-10-30T10:03:02.683455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:02.811464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

dteday
Categorical

HIGH CARDINALITY
UNIFORM

Distinct731
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size137.2 KiB
2012-09-09
 
24
2012-07-01
 
24
2011-04-21
 
24
2012-08-01
 
24
2012-08-16
 
24
Other values (726)
17424 
ValueCountFrequency (%) 
2012-09-09240.1%
 
2012-07-01240.1%
 
2011-04-21240.1%
 
2012-08-01240.1%
 
2012-08-16240.1%
 
2012-03-30240.1%
 
2011-07-13240.1%
 
2011-06-08240.1%
 
2011-12-26240.1%
 
2011-02-07240.1%
 
2011-10-07240.1%
 
2012-02-18240.1%
 
2012-01-29240.1%
 
2011-04-26240.1%
 
2012-04-28240.1%
 
2011-10-17240.1%
 
2012-03-21240.1%
 
2012-09-28240.1%
 
2011-01-12240.1%
 
2012-06-23240.1%
 
2011-03-24240.1%
 
2012-03-28240.1%
 
2012-12-27240.1%
 
2011-08-28240.1%
 
2012-06-20240.1%
 
Other values (706)1694496.6%
 
2020-10-30T10:03:02.962583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T10:03:03.081540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
14147223.6%
 
03902422.2%
 
23664820.9%
 
-3508820.0%
 
340802.3%
 
532161.8%
 
732161.8%
 
832161.8%
 
431681.8%
 
631681.8%
 
931441.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number14035280.0%
 
Dash Punctuation3508820.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14147229.5%
 
03902427.8%
 
23664826.1%
 
340802.9%
 
532162.3%
 
732162.3%
 
832162.3%
 
431682.3%
 
631682.3%
 
931442.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-35088100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common175440100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
14147223.6%
 
03902422.2%
 
23664820.9%
 
-3508820.0%
 
340802.3%
 
532161.8%
 
732161.8%
 
832161.8%
 
431681.8%
 
631681.8%
 
931441.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII175440100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
14147223.6%
 
03902422.2%
 
23664820.9%
 
-3508820.0%
 
340802.3%
 
532161.8%
 
732161.8%
 
832161.8%
 
431681.8%
 
631681.8%
 
931441.8%
 

season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.2 KiB
3
4512 
2
4416 
1
4344 
4
4272 
ValueCountFrequency (%) 
3451225.7%
 
2441625.2%
 
1434424.8%
 
4427224.4%
 
2020-10-30T10:03:03.179092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-30T10:03:03.242730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:03.327307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.1754433.3%
 
01754433.3%
 
345128.6%
 
244168.4%
 
143448.3%
 
442728.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3508866.7%
 
Other Punctuation1754433.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01754450.0%
 
3451212.9%
 
2441612.6%
 
1434412.4%
 
4427212.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.17544100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common52632100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.1754433.3%
 
01754433.3%
 
345128.6%
 
244168.4%
 
143448.3%
 
442728.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII52632100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.1754433.3%
 
01754433.3%
 
345128.6%
 
244168.4%
 
143448.3%
 
442728.1%
 

yr
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.2 KiB
1
8784 
0
8760 
ValueCountFrequency (%) 
1878450.1%
 
0876049.9%
 
2020-10-30T10:03:03.391977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

mnth
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.519835841
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size137.2 KiB
2020-10-30T10:03:03.456852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.449649205
Coefficient of variation (CV)0.529100623
Kurtosis-1.209056868
Mean6.519835841
Median Absolute Deviation (MAD)3
Skewness-0.008132615041
Sum114384
Variance11.90007964
MonotocityNot monotonic
2020-10-30T10:03:03.549904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
1214888.5%
 
1014888.5%
 
814888.5%
 
714888.5%
 
514888.5%
 
314888.5%
 
114888.5%
 
1114408.2%
 
914408.2%
 
614408.2%
 
414408.2%
 
213687.8%
 
ValueCountFrequency (%) 
114888.5%
 
213687.8%
 
314888.5%
 
414408.2%
 
514888.5%
 
614408.2%
 
714888.5%
 
814888.5%
 
914408.2%
 
1014888.5%
 
ValueCountFrequency (%) 
1214888.5%
 
1114408.2%
 
1014888.5%
 
914408.2%
 
814888.5%
 
714888.5%
 
614408.2%
 
514888.5%
 
414408.2%
 
314888.5%
 

hr
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros731
Zeros (%)4.2%
Memory size137.2 KiB
2020-10-30T10:03:03.647381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.922383842
Coefficient of variation (CV)0.601946421
Kurtosis-1.204175095
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum201756
Variance47.91939805
MonotocityNot monotonic
2020-10-30T10:03:03.750554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
157314.2%
 
147314.2%
 
167314.2%
 
17314.2%
 
177314.2%
 
27314.2%
 
187314.2%
 
37314.2%
 
197314.2%
 
47314.2%
 
207314.2%
 
57314.2%
 
217314.2%
 
67314.2%
 
227314.2%
 
77314.2%
 
237314.2%
 
87314.2%
 
97314.2%
 
107314.2%
 
117314.2%
 
127314.2%
 
137314.2%
 
07314.2%
 
ValueCountFrequency (%) 
07314.2%
 
17314.2%
 
27314.2%
 
37314.2%
 
47314.2%
 
57314.2%
 
67314.2%
 
77314.2%
 
87314.2%
 
97314.2%
 
ValueCountFrequency (%) 
237314.2%
 
227314.2%
 
217314.2%
 
207314.2%
 
197314.2%
 
187314.2%
 
177314.2%
 
167314.2%
 
157314.2%
 
147314.2%
 

holiday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.2 KiB
0
17022 
1
 
522
ValueCountFrequency (%) 
01702297.0%
 
15223.0%
 
2020-10-30T10:03:03.824088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.002735978
Minimum0
Maximum6
Zeros2520
Zeros (%)14.4%
Memory size137.2 KiB
2020-10-30T10:03:04.049426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.003472285
Coefficient of variation (CV)0.6672155991
Kurtosis-1.253931627
Mean3.002735978
Median Absolute Deviation (MAD)2
Skewness-0.002736202689
Sum52680
Variance4.013901196
MonotocityNot monotonic
2020-10-30T10:03:04.129336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6252014.4%
 
5252014.4%
 
0252014.4%
 
4249614.2%
 
3249614.2%
 
2249614.2%
 
1249614.2%
 
ValueCountFrequency (%) 
0252014.4%
 
1249614.2%
 
2249614.2%
 
3249614.2%
 
4249614.2%
 
5252014.4%
 
6252014.4%
 
ValueCountFrequency (%) 
6252014.4%
 
5252014.4%
 
4249614.2%
 
3249614.2%
 
2249614.2%
 
1249614.2%
 
0252014.4%
 

workingday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.2 KiB
1
11982 
0
5562 
ValueCountFrequency (%) 
11198268.3%
 
0556231.7%
 
2020-10-30T10:03:04.201912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

weathersit
Real number (ℝ≥0)

Distinct46
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.432398541
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Memory size137.2 KiB
2020-10-30T10:03:04.282160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.645304696
Coefficient of variation (CV)0.4505063903
Kurtosis0.2842194266
Mean1.432398541
Median Absolute Deviation (MAD)0
Skewness1.209671128
Sum25130
Variance0.4164181507
MonotocityNot monotonic
2020-10-30T10:03:04.400573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
11145265.3%
 
2455926.0%
 
314788.4%
 
2.57< 0.1%
 
1.55< 0.1%
 
43< 0.1%
 
2.6153846151< 0.1%
 
2.9130434781< 0.1%
 
2.2857142861< 0.1%
 
2.2307692311< 0.1%
 
2.6521739131< 0.1%
 
1.2608695651< 0.1%
 
1.8695652171< 0.1%
 
2.3076923081< 0.1%
 
2.3913043481< 0.1%
 
2.5714285711< 0.1%
 
1.0869565221< 0.1%
 
2.1304347831< 0.1%
 
2.0769230771< 0.1%
 
2.1538461541< 0.1%
 
2.5384615381< 0.1%
 
1.4347826091< 0.1%
 
2.8571428571< 0.1%
 
2.9230769231< 0.1%
 
1.6086956521< 0.1%
 
Other values (21)210.1%
 
ValueCountFrequency (%) 
11145265.3%
 
1.0869565221< 0.1%
 
1.1739130431< 0.1%
 
1.2608695651< 0.1%
 
1.3478260871< 0.1%
 
1.4347826091< 0.1%
 
1.55< 0.1%
 
1.521739131< 0.1%
 
1.6086956521< 0.1%
 
1.6956521741< 0.1%
 
ValueCountFrequency (%) 
43< 0.1%
 
314788.4%
 
2.9230769231< 0.1%
 
2.9130434781< 0.1%
 
2.8571428571< 0.1%
 
2.8461538461< 0.1%
 
2.8260869571< 0.1%
 
2.7692307691< 0.1%
 
2.7391304351< 0.1%
 
2.7142857141< 0.1%
 

temp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct165
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4950444596
Minimum0.02
Maximum1
Zeros0
Zeros (%)0.0%
Memory size137.2 KiB
2020-10-30T10:03:04.530100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.2
Q10.34
median0.5
Q30.66
95-th percentile0.8
Maximum1
Range0.98
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.1931923279
Coefficient of variation (CV)0.3902524796
Kurtosis-0.9456811262
Mean0.4950444596
Median Absolute Deviation (MAD)0.16
Skewness-0.0005768974047
Sum8685.06
Variance0.03732327555
MonotocityNot monotonic
2020-10-30T10:03:04.665526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.627274.1%
 
0.666934.0%
 
0.646923.9%
 
0.76903.9%
 
0.66753.8%
 
0.366713.8%
 
0.346463.7%
 
0.36423.7%
 
0.46143.5%
 
0.326143.5%
 
0.565793.3%
 
0.725703.2%
 
0.545703.2%
 
0.465603.2%
 
0.265593.2%
 
0.525563.2%
 
0.425493.1%
 
0.55313.0%
 
0.245253.0%
 
0.745162.9%
 
0.445082.9%
 
0.224222.4%
 
0.763962.3%
 
0.383722.1%
 
0.23622.1%
 
Other values (140)330518.8%
 
ValueCountFrequency (%) 
0.02180.1%
 
0.031< 0.1%
 
0.04170.1%
 
0.06170.1%
 
0.08170.1%
 
0.086666666671< 0.1%
 
0.093333333331< 0.1%
 
0.1520.3%
 
0.12770.4%
 
0.13142857141< 0.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.981< 0.1%
 
0.96160.1%
 
0.94170.1%
 
0.92490.3%
 
0.9900.5%
 
0.88530.3%
 
0.861310.7%
 
0.841380.8%
 
0.822131.2%
 

atemp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct177
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.473957028
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Memory size137.2 KiB
2020-10-30T10:03:04.808207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.197
Q10.3333
median0.4848
Q30.6212
95-th percentile0.7424
Maximum1
Range1
Interquartile range (IQR)0.2879

Descriptive statistics

Standard deviation0.1725194316
Coefficient of variation (CV)0.3639980449
Kurtosis-0.8529035073
Mean0.473957028
Median Absolute Deviation (MAD)0.1364
Skewness-0.08502366103
Sum8315.1021
Variance0.02976295427
MonotocityNot monotonic
2020-10-30T10:03:04.954920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.62129885.6%
 
0.51526193.5%
 
0.40916143.5%
 
0.33336013.4%
 
0.66675933.4%
 
0.60615883.4%
 
0.53035793.3%
 
0.55753.3%
 
0.45455603.2%
 
0.3035503.1%
 
0.42425493.1%
 
0.48485313.0%
 
0.43945072.9%
 
0.65154972.8%
 
0.54554932.8%
 
0.22734712.7%
 
0.63644522.6%
 
0.34854442.5%
 
0.6974432.5%
 
0.28794382.5%
 
0.25764232.4%
 
0.59094122.3%
 
0.31824082.3%
 
0.27273972.3%
 
0.68183812.2%
 
Other values (152)443125.3%
 
ValueCountFrequency (%) 
02< 0.1%
 
0.01524< 0.1%
 
0.022751< 0.1%
 
0.03038< 0.1%
 
0.03791< 0.1%
 
0.045590.1%
 
0.0606140.1%
 
0.06821< 0.1%
 
0.0758280.2%
 
0.0909130.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.98482< 0.1%
 
0.95451< 0.1%
 
0.92425< 0.1%
 
0.90915< 0.1%
 
0.8939150.1%
 
0.8788190.1%
 
0.8636200.1%
 
0.8485320.2%
 
0.8333410.2%
 

hum
Real number (ℝ≥0)

Distinct190
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6284276676
Minimum0
Maximum1
Zeros24
Zeros (%)0.1%
Memory size137.2 KiB
2020-10-30T10:03:05.098691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.31
Q10.48
median0.63
Q30.79
95-th percentile0.93
Maximum1
Range1
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.1930411904
Coefficient of variation (CV)0.3071812404
Kurtosis-0.8220004959
Mean0.6284276676
Median Absolute Deviation (MAD)0.15
Skewness-0.1227933778
Sum11025.135
Variance0.03726490121
MonotocityNot monotonic
2020-10-30T10:03:05.248027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.886573.7%
 
0.836303.6%
 
0.945633.2%
 
0.874902.8%
 
0.74302.5%
 
0.663882.2%
 
0.653872.2%
 
0.693612.1%
 
0.553542.0%
 
0.743411.9%
 
0.613371.9%
 
0.773361.9%
 
0.933341.9%
 
0.783271.9%
 
0.493271.9%
 
0.623261.9%
 
0.463171.8%
 
0.733171.8%
 
0.523121.8%
 
0.563111.8%
 
0.822991.7%
 
0.412901.7%
 
0.542881.6%
 
0.812761.6%
 
0.592731.6%
 
Other values (165)827347.2%
 
ValueCountFrequency (%) 
0240.1%
 
0.081< 0.1%
 
0.11< 0.1%
 
0.121< 0.1%
 
0.131< 0.1%
 
0.142< 0.1%
 
0.154< 0.1%
 
0.16100.1%
 
0.17100.1%
 
0.18100.1%
 
ValueCountFrequency (%) 
12711.5%
 
0.971< 0.1%
 
0.963< 0.1%
 
0.945633.2%
 
0.933341.9%
 
0.922< 0.1%
 
0.91347826091< 0.1%
 
0.911< 0.1%
 
0.912< 0.1%
 
0.990.1%
 

windspeed
Real number (ℝ≥0)

ZEROS

Distinct121
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1909653129
Minimum0
Maximum0.8507
Zeros2183
Zeros (%)12.4%
Memory size137.2 KiB
2020-10-30T10:03:05.390442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1045
median0.194
Q30.2537
95-th percentile0.4179
Maximum0.8507
Range0.8507
Interquartile range (IQR)0.1492

Descriptive statistics

Standard deviation0.1228999275
Coefficient of variation (CV)0.6435719952
Kurtosis0.642089714
Mean0.1909653129
Median Absolute Deviation (MAD)0.0895
Skewness0.5858050618
Sum3350.29545
Variance0.01510439217
MonotocityNot monotonic
2020-10-30T10:03:05.541854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0218312.4%
 
0.134317429.9%
 
0.164216969.7%
 
0.19416589.5%
 
0.104516199.2%
 
0.223915138.6%
 
0.089614298.1%
 
0.253712957.4%
 
0.283610486.0%
 
0.29858084.6%
 
0.32846023.4%
 
0.35824972.8%
 
0.38813822.2%
 
0.41792961.7%
 
0.44781761.0%
 
0.46271460.8%
 
0.49251120.6%
 
0.5224840.5%
 
0.5821440.3%
 
0.5522390.2%
 
0.6119230.1%
 
0.6418140.1%
 
0.6567110.1%
 
0.26125< 0.1%
 
0.68665< 0.1%
 
Other values (96)1170.7%
 
ValueCountFrequency (%) 
0218312.4%
 
0.04482< 0.1%
 
0.052254< 0.1%
 
0.067152< 0.1%
 
0.08211< 0.1%
 
0.089614298.1%
 
0.097051< 0.1%
 
0.104516199.2%
 
0.111951< 0.1%
 
0.11944< 0.1%
 
ValueCountFrequency (%) 
0.85072< 0.1%
 
0.83581< 0.1%
 
0.8062< 0.1%
 
0.80168571431< 0.1%
 
0.77611< 0.1%
 
0.76757142861< 0.1%
 
0.74632< 0.1%
 
0.73345714291< 0.1%
 
0.71642< 0.1%
 
0.69934285711< 0.1%
 

casual
Real number (ℝ≥0)

ZEROS

Distinct410
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.35855563
Minimum0
Maximum367
Zeros1623
Zeros (%)9.3%
Memory size137.2 KiB
2020-10-30T10:03:05.675171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median16
Q348
95-th percentile138
Maximum367
Range367
Interquartile range (IQR)44

Descriptive statistics

Standard deviation49.1815934
Coefficient of variation (CV)1.390938983
Kurtosis7.643877527
Mean35.35855563
Median Absolute Deviation (MAD)15
Skewness2.510684406
Sum620330.5
Variance2418.829129
MonotocityNot monotonic
2020-10-30T10:03:05.814840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
016239.3%
 
110956.2%
 
28004.6%
 
36974.0%
 
45613.2%
 
55092.9%
 
64482.6%
 
74052.3%
 
83782.2%
 
93482.0%
 
103171.8%
 
113151.8%
 
122991.7%
 
132581.5%
 
142331.3%
 
152251.3%
 
172191.2%
 
162181.2%
 
182121.2%
 
192061.2%
 
271931.1%
 
201891.1%
 
211731.0%
 
311640.9%
 
251580.9%
 
Other values (385)730141.6%
 
ValueCountFrequency (%) 
016239.3%
 
0.043478260871< 0.1%
 
0.076923076921< 0.1%
 
0.086956521741< 0.1%
 
0.13043478261< 0.1%
 
0.14285714291< 0.1%
 
0.15384615381< 0.1%
 
0.17391304351< 0.1%
 
0.21739130431< 0.1%
 
0.23076923081< 0.1%
 
ValueCountFrequency (%) 
3671< 0.1%
 
3621< 0.1%
 
3611< 0.1%
 
3571< 0.1%
 
3561< 0.1%
 
3551< 0.1%
 
3541< 0.1%
 
3521< 0.1%
 
3501< 0.1%
 
3471< 0.1%
 

registered
Real number (ℝ≥0)

HIGH CORRELATION

Distinct867
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.5279298
Minimum0
Maximum886
Zeros24
Zeros (%)0.1%
Memory size137.2 KiB
2020-10-30T10:03:05.964732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q133
median114
Q3219
95-th percentile463
Maximum886
Range886
Interquartile range (IQR)186

Descriptive statistics

Standard deviation151.2185237
Coefficient of variation (CV)0.9914153028
Kurtosis2.77544819
Mean152.5279298
Median Absolute Deviation (MAD)89
Skewness1.565279375
Sum2675950
Variance22867.04191
MonotocityNot monotonic
2020-10-30T10:03:06.099788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
43121.8%
 
33111.8%
 
52931.7%
 
62671.5%
 
22541.4%
 
12051.2%
 
72031.2%
 
81911.1%
 
91781.0%
 
111400.8%
 
101330.8%
 
191060.6%
 
221050.6%
 
141020.6%
 
20960.5%
 
15950.5%
 
13930.5%
 
16920.5%
 
23900.5%
 
12900.5%
 
17850.5%
 
24830.5%
 
26830.5%
 
21810.5%
 
18800.5%
 
Other values (842)1377678.5%
 
ValueCountFrequency (%) 
0240.1%
 
12051.2%
 
1.3333333331< 0.1%
 
1.3333333331< 0.1%
 
1.5100.1%
 
1.6666666671< 0.1%
 
1.6666666671< 0.1%
 
1.9285714291< 0.1%
 
22541.4%
 
2.3333333331< 0.1%
 
ValueCountFrequency (%) 
8861< 0.1%
 
8851< 0.1%
 
8762< 0.1%
 
8711< 0.1%
 
8601< 0.1%
 
8572< 0.1%
 
8391< 0.1%
 
8381< 0.1%
 
8331< 0.1%
 
8221< 0.1%
 

cnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct974
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.8864854
Minimum1
Maximum977
Zeros0
Zeros (%)0.0%
Memory size137.2 KiB
2020-10-30T10:03:06.237753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q138
median140
Q3279
95-th percentile562
Maximum977
Range976
Interquartile range (IQR)241

Descriptive statistics

Standard deviation181.2782009
Coefficient of variation (CV)0.9648283138
Kurtosis1.438412688
Mean187.8864854
Median Absolute Deviation (MAD)112
Skewness1.285737958
Sum3296280.5
Variance32861.78612
MonotocityNot monotonic
2020-10-30T10:03:06.378260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
52671.5%
 
42391.4%
 
62371.4%
 
32301.3%
 
22111.2%
 
72021.2%
 
81831.0%
 
11600.9%
 
101550.9%
 
111470.8%
 
91290.7%
 
121220.7%
 
131130.6%
 
161050.6%
 
171020.6%
 
14970.6%
 
20910.5%
 
26900.5%
 
21900.5%
 
15890.5%
 
28860.5%
 
23810.5%
 
18790.5%
 
24760.4%
 
31750.4%
 
Other values (949)1408880.3%
 
ValueCountFrequency (%) 
11600.9%
 
1.3333333331< 0.1%
 
1.57< 0.1%
 
1.6666666671< 0.1%
 
1.6666666671< 0.1%
 
22111.2%
 
2.0714285711< 0.1%
 
2.3333333332< 0.1%
 
2.5120.1%
 
2.7142857141< 0.1%
 
ValueCountFrequency (%) 
9771< 0.1%
 
9761< 0.1%
 
9701< 0.1%
 
9681< 0.1%
 
9671< 0.1%
 
9631< 0.1%
 
9571< 0.1%
 
9531< 0.1%
 
9481< 0.1%
 
9431< 0.1%
 

Interactions

2020-10-30T10:02:47.847830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:47.963648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:48.067393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:48.176399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:48.286440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:48.393927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:48.600280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:48.706158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:48.808385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:48.919530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.019942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.120029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.220021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.315459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.416560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.518651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.616933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.714729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.812476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:49.911322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.017308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.116151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.214930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.322743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.435976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.555804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.675507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.792376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:50.912414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.028672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.148846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.275920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.391640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.512401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.629188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.744729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.867393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:51.990986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:52.109114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:52.229370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:52.345520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:52.461946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:52.586905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:52.812781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:52.932081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.046264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.156999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.274445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.391494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.506018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.619826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.734863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.844026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:53.963947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.074182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.188029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.299124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.408202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.525378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.644192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.757520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.873057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:54.988014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:55.099849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:55.221137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:55.330917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:55.446414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:55.559315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:55.669468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:55.785192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:55.904143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.018660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.133047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.252224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.362645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.482712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.593990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.705634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.819045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:56.924839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.036620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.149414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.258921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.369828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.479876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.587209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.704477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.811175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:57.919252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:58.171214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:58.289436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:58.411726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:58.538230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:58.658791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:58.778125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:58.900223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.020770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.148481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.267390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.388889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.498707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.604462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.717410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.831728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:02:59.941713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.051821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.163522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.269406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.384956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.493984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.602717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.713697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.822109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:00.937515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:01.053321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:01.166580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:01.281777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:01.395415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:01.505518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:01.624157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:01.733397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-30T10:03:06.516658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-30T10:03:06.758662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-30T10:03:06.963062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-30T10:03:07.172671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-30T10:03:01.985016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-30T10:03:02.382613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

datetimedtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
02011-01-01 00:00:002011-01-011.00.0100.050.01.00.240.28790.810.00003.013.016.0
12011-01-01 01:00:002011-01-011.00.0110.050.01.00.220.27270.800.00008.032.040.0
22011-01-01 02:00:002011-01-011.00.0120.050.01.00.220.27270.800.00005.027.032.0
32011-01-01 03:00:002011-01-011.00.0130.050.01.00.240.28790.750.00003.010.013.0
42011-01-01 04:00:002011-01-011.00.0140.050.01.00.240.28790.750.00000.01.01.0
52011-01-01 05:00:002011-01-011.00.0150.050.02.00.240.25760.750.08960.01.01.0
62011-01-01 06:00:002011-01-011.00.0160.050.01.00.220.27270.800.00002.00.02.0
72011-01-01 07:00:002011-01-011.00.0170.050.01.00.200.25760.860.00001.02.03.0
82011-01-01 08:00:002011-01-011.00.0180.050.01.00.240.28790.750.00001.07.08.0
92011-01-01 09:00:002011-01-011.00.0190.050.01.00.320.34850.760.00008.06.014.0

Last rows

datetimedtedayseasonyrmnthhrholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
175342012-12-31 14:00:002012-12-311.01.012140.001.02.00.280.27270.450.223962.0185.0247.0
175352012-12-31 15:00:002012-12-311.01.012150.001.02.00.280.28790.450.134369.0246.0315.0
175362012-12-31 16:00:002012-12-311.01.012160.001.02.00.260.25760.480.194030.0184.0214.0
175372012-12-31 17:00:002012-12-311.01.012170.001.02.00.260.28790.480.089614.0150.0164.0
175382012-12-31 18:00:002012-12-311.01.012180.001.02.00.260.27270.480.134310.0112.0122.0
175392012-12-31 19:00:002012-12-311.01.012190.001.02.00.260.25760.600.164211.0108.0119.0
175402012-12-31 20:00:002012-12-311.01.012200.001.02.00.260.25760.600.16428.081.089.0
175412012-12-31 21:00:002012-12-311.01.012210.001.01.00.260.25760.600.16427.083.090.0
175422012-12-31 22:00:002012-12-311.01.012220.001.01.00.260.27270.560.134313.048.061.0
175432012-12-31 23:00:002012-12-311.01.012230.001.01.00.260.27270.650.134312.037.049.0